NOTIFICACIÓN PERSONAL PRESENCIAL Y CÓMPUTO DEL TÉRMINO PARA CONTESTAR LA DEMANDA: IMPROCEDENCIA DE APLICAR LAS REGLAS DE NOTIFICACIÓN ELECTRÓNICA DE LA LEY 2213 DE 2022 CUANDO LA NOTIFICACIÓN SE REALIZÓ CONFORME AL ARTÍCULO 291 DEL CÓDIGO GENERAL DEL PROCESO.
🧵 CSJ | SP601-2026 | Rad. 68470 | M.P. Carlos Roberto Solórzano Garavito
¿Qué significa realmente valorar la prueba de manera integral?
La Corte Suprema acaba de dictar una sentencia muy pedagógica sobre uno de los errores más frecuentes en la valoración probatoria: construir la convicción únicamente con la prueba que confirma una hipótesis, sin explicar o considerar el alcance de aquella que la contradice.
Abro hilo. 👇
University of Queenland's 123-page guide on how to develop academic writing skills
Click the link below to download the guide for free.
Follow Silvi on LinkedIn for more free resources on academic writing.
https://t.co/cr9nqy5Yda
LA NOTIFICACIÓN ELECTRÓNICA SE ENTIENDE SURTIDA CUANDO EL MENSAJE LLEGA AL BUZÓN ELECTRÓNICO DEL DESTINATARIO, SIN QUE SEA NECESARIO ACREDITAR SU LECTURA O ACUSE DE RECIBO
🧑⚖️ DESEAS ESTAR ACTUALIZADO A DIARIO DE TODA LA JURISPRUDENCIA DEL PAIS? 👉https://t.co/pbsHVXKcKz
A student submitted an essay she wrote by hand. Her university ran it through an AI detector. The detector said she cheated. She is autistic.
Her name is Moira Olmsted. Adelphi University. February 2026. Turnitin flagged her essay as 100% AI-generated. She was disciplined.
Two other AI detectors classified the same essay as human-written.
She sued. She won. The court called the school's decision "arbitrary and capricious."
She is not the only one.
In May 2026, a high school student in Palo Alto was expelled after an AI detector flagged his work. He faced visa revocation. He filed a federal civil rights lawsuit.
A researcher at Griffith University just proved mathematically why this keeps happening. The paper is on arXiv. The finding is one sentence.
AI text detectors have a structural flaw that no amount of better engineering can fix.
Here is what the math says.
If a university wants its detector to catch 80% of cheaters, at least 750 out of every 10,000 innocent students will be wrongly accused. That is not a software problem. It is a theorem.
If the university tries to limit false accusations to 1%, detection power collapses to 6%. It catches 6 out of every 100 AI-written papers. The other 94 get through.
There is no setting where the detector is both fair and effective.
The reason is diversity. Every student writes differently. Non-native English speakers use simpler vocabulary. Shorter sentences. Clearer structures. So does AI. A Stanford study found that 61.3% of TOEFL essays written by non-native English speakers were misclassified as AI-generated. A separate analysis tested 14 commercial detection tools. Zero out of 14 reached 80% accuracy.
The students most likely to be wrongly accused are non-native English speakers, neurodivergent students, and anyone who writes with clarity and precision. The qualities that make their writing effective are the same qualities the detector mistakes for a machine.
Vanderbilt University understood this. They disabled Turnitin's AI detection in 2023 after calculating that even a 1% error rate across 75,000 submissions would produce 750 wrongful accusations per year.
750 students accused of cheating for writing like themselves.
The paper's conclusion is not that we need better detectors. It is that the diversity of human writing itself makes accurate detection mathematically impossible.
The same thing that makes your writing yours is the thing that gets you accused.
https://t.co/L91ldtXP05
Guía metodológica completa para investigadores que realizan revisiones sistemáticas cuantitativas y metaanálisis utilizando el lenguaje de programación R e introduce el metaanálisis multinivel.
🔗https://t.co/iJ43AEF92w
TU EMAIL ES UNA BASE DE DATOS SECRETA Y NO LO SABES
Felix, un ingeniero de Anthropic se mudaba de casa.
en vez de medir mueble por mueble a mano…
le pidió a Claude que mirara su correo.
cada factura.
cada confirmación de compra.
cada email de "tu pedido ha llegado".
ahí estaban las medidas exactas de TODOS sus muebles.
llevaban años metidas en su bandeja de entrada.
Claude lo leyó todo y le montó un plano 3D de su casa.
con sus muebles reales. a escala.
y aquí está lo que tienes que pillar:
esto no va solo de muebles.
→ tu ropa: años de pedidos con tallas y marcas
→ tus viajes: vuelos, hoteles, fechas, todo
→ tus gastos: cada suscripción que pagas sin recordar
→ tu salud: análisis, recetas, citas
llevas años construyendo una base de datos sobre tu vida.
sin darte cuenta.
solo que nunca habías tenido cómo leerla.
ahora sí.
la próxima vez que hagas algo a mano…
para y pregúntate si Claude puede sacarlo de tu correo.
en esta entrevista lo explica ⬇️